code: def prepare_data(blending=False):
      col_types = [
          "numeric",
          "numeric",
          "numeric",
          "enum",
          "enum",
          "numeric",
          "numeric",
          "numeric",
          "numeric",
      ]
      dat = h2o.upload_file(
          path=pu.locate("smalldata/extdata/prostate.csv"),
          destination_frame="prostate_hex",
          col_types=col_types,
      )
      train, test = dat.split_frame(ratios=[0.8], seed=1)
      x = ["CAPSULE", "GLEASON", "RACE", "DPROS", "DCAPS", "PSA", "VOL"]
      y = "AGE"
      ds = pu.ns(x=x, y=y, train=train, test=test)
  
      if blending:
          train, blend = train.split_frame(ratios=[0.7], seed=seed)
          return ds.extend(train=train, blend=blend)
      else:
          return ds
  
file_path: h2o-3/h2o-py/tests/testdir_algos/stackedensemble/pyunit_stackedensemble_gaussian.py
imports: import h2o
  import sys
  from h2o.estimators.random_forest import H2ORandomForestEstimator
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
  from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
  from tests import pyunit_utils
  from tests.pyunit_utils import assert_warn
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_data(blending) and returns: ds, ds